
import numpy as np
import pandas as pd

import plotly.express as px
import plotly.graph_objects as go
import plotly.figure_factory as ff
import plotly.subplots as sp

import matplotlib.pyplot as plt
import seaborn as sns

from collections import Counter
from plotly.offline import init_notebook_mode,iplot


df = pd.read_csv('vgsales.csv')
df.head()


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 16598 entries, 0 to 16597
Data columns (total 11 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   Rank          16598 non-null  int64  
 1   Name          16598 non-null  object 
 2   Platform      16598 non-null  object 
 3   Year          16327 non-null  float64
 4   Genre         16598 non-null  object 
 5   Publisher     16540 non-null  object 
 6   NA_Sales      16598 non-null  float64
 7   EU_Sales      16598 non-null  float64
 8   JP_Sales      16598 non-null  float64
 9   Other_Sales   16598 non-null  float64
 10  Global_Sales  16598 non-null  float64
dtypes: float64(6), int64(1), object(4)
memory usage: 1.4+ MB


df.describe()


df.isnull().sum()

Rank              0
Name              0
Platform          0
Year            271
Genre             0
Publisher        58
NA_Sales          0
EU_Sales          0
JP_Sales          0
Other_Sales       0
Global_Sales      0
dtype: int64


df = df.dropna()


# Make a Bar plot of 10 most common Platforms
top_platforms = df['Platform'].value_counts().index[:10]

plt.style.use("ggplot")
top_platforms_df = df[df['Platform'].isin(top_platforms)]
sns.countplot(data=top_platforms_df, x='Platform', order=top_platforms)
plt.title("Top 10 Platforms by Count")
plt.xlabel("Platform Name")
plt.ylabel("Count")
plt.show()


# Select the top 100 games from the dataframe
top100_games = df.head(100)

# Initialize a plotly graph object Figure
top100_game_sales_per_region = go.Figure()

# Define the regions, their corresponding colors and names
regions = ['NA_Sales', 'EU_Sales', 'JP_Sales', 'Other_Sales']
colors = ['#d32e4d', '#4fb3f9', '#55bca1', '#e4d747']
names = ['North America', 'Europe', 'Japan', 'Other']

# Loop over each region
for region, color, name in zip(regions, colors, names):
    # Add a scatter plot trace for each region
    top100_game_sales_per_region.add_trace(go.Scatter(
        x=top100_games['Rank'], 
        y=top100_games[region], 
        mode='markers',  
        marker=dict(color=color, size=10), 
        name=name,  
        text=top100_games['Name']  
    ))

# Update the layout of the plot
top100_game_sales_per_region.update_layout(
    title='Top 100 Video Games Sales by Region', 
    xaxis=dict(title='Rank'),  
    yaxis=dict(title='Sales(In Millions)'),  
    paper_bgcolor='#fdfdfd',  
    plot_bgcolor='#fdfdfd',  
    hovermode='x'  
)

# Display the plot
top100_game_sales_per_region.show()


# Initialize an empty dictionary to hold the aggregation operations for each region
regions_agg = {}

# For each region, specify that we want to sum the sales
for region in regions:
    regions_agg[region] = 'sum'

# Group the dataframe by year and apply the operations (sum of sales) for each region
geo_tdf = df.groupby(['Year']).agg(regions_agg).reset_index()

sales_by_region_line_graph = go.Figure()

# For each region, add a line trace to the graph
for region in regions:
    sales_by_region_line_graph.add_trace(go.Scatter(
        x=geo_tdf['Year'],  
        y=geo_tdf[region],  
        mode='lines',   
        name=region,  
    ))

# Update the layout of the graph
sales_by_region_line_graph.update_layout(
    title="Total sales per year by region",  
    xaxis_title="Year",  
    yaxis_title="Sales (Millions)",  
    paper_bgcolor='#fdfdfd',  
)

# Display the graph
sales_by_region_line_graph.show()


# Create a new column that combines the 'Name' and 'Year' columns
top100_games['Name_and_Year'] = top100_games['Name'] + ' (' + top100_games['Year'].astype('Int64').astype(str) + ')'

# Create a bubble graph
release_year_top100_bubble_graph = px.scatter(top100_games, x='Rank', y='Year',
                 size='Global_Sales', color='Global_Sales',
                 color_continuous_scale='Blackbody',
                 labels={'x':'Rank', 'y':'Years'},
                 title='Release Years of Top 100 Video Games According to Global Sales',
                 hover_name='Name_and_Year',  # Use the new column as the hover name
                 hover_data=['Publisher', 'Platform', 'Genre', 'Global_Sales'])  # Additional data to show on hover

# Update the layout of the plot
release_year_top100_bubble_graph.update_layout(
    paper_bgcolor='#fdfdfd', 
    plot_bgcolor='#fdfdfd', 
    hovermode='x',  
)

# Display the plot
release_year_top100_bubble_graph.show()


# Count the number of games by each publisher
publisher_counts = top100_games['Publisher'].value_counts().reset_index()
publisher_counts.columns = ['Publisher', 'Count']

# Create a pie chart
publisher_top100_pie_chart = px.pie(publisher_counts, values='Count', names='Publisher', title='Top 100 Video Games Publisher Distribution')
publisher_top100_pie_chart.show()


# Group the dataframe by 'Publisher', calculate the sum of 'Global_Sales' for each,
# sort the results in descending order, and select the top 10
top10_publishers_by_sales = df.groupby('Publisher')['Global_Sales'].sum().sort_values(ascending=False).head(10)
top10_publishers_by_sales_df = top10_publishers_by_sales.reset_index() # Convert the Series to a DataFrame

# Create a bar plot
plt.figure(figsize=(10, 6))
sns.barplot(x='Global_Sales', y='Publisher', data=top10_publishers_by_sales_df, palette='viridis')
plt.title('Top 10 Publishers by Global Sales')
plt.xlabel('Global Sales (in millions)')
plt.ylabel('Publisher')
plt.show()


most_common_platforms = df['Platform'].value_counts().head(10).reset_index()
most_common_platforms.columns = ['Platform', 'Count']

fig = px.bar(most_common_platforms, x='Platform', y='Count', title='Top 10 Most Common Platforms',
             color='Platform', color_discrete_sequence=px.colors.qualitative.Pastel)
fig.show()


# Use a pastel color palette
color_palette = px.colors.qualitative.Light24

# Calculate the genre counts for each platform
platform_genre_counts = df['Platform'].value_counts().sort_values(ascending=False)

# Create the histogram
fig = px.histogram(df, x='Platform', color='Genre', barmode='stack', category_orders={'Platform': platform_genre_counts.index}, color_discrete_sequence=color_palette)

# Update the layout
fig.update_layout(
    title='Genre Counts According to Platform',
    xaxis_title='Platform',
    yaxis_title='Count',
    paper_bgcolor='beige',
    plot_bgcolor='beige',
)

fig.show()


# Define a color dictionary
color_dict = {
    'NA_Sales': '#1f77b4',
    'EU_Sales': '#ff7f0e',
    'JP_Sales': '#2ca02c',
    'Other_Sales': '#d62728'
}

# Define the regions names for display
region_names = ['North America', 'Europe', 'Japan', 'Other Regions']

#Creating subplots
fig = sp.make_subplots(rows=2, cols=2, subplot_titles=region_names, vertical_spacing=0.15)

# Iterate over each region and add a bar chart to the subplot
for i, region in enumerate(regions, start=1):
    # Calculate the genre counts for each region
    region_genre_counts = df.groupby('Genre')[region].sum().sort_values(ascending=True)

    region_name = ['North America', 'Europe', 'Japan', 'Other Regions']

    # Create the histogram
    bar_chart = go.Bar(
        x=region_genre_counts.values,
        y=region_genre_counts.index,
        orientation='h',
        marker_color=color_dict[region],
        name=region_names[i-1],
        text=np.round(region_genre_counts.values),
        hoverinfo='none'
    )

    # Add the histogram to the subplot
    fig.add_trace(bar_chart, row=(i-1)//2+1, col=(i-1)%2+1)
    fig.update_xaxes(title_text="sales (in mil)", row=(i-1)//2+1, col=(i-1)%2+1)

# Update the layout
fig.update_layout(
    title='Total Sales According to Genre and Region',
    paper_bgcolor='#fdfdfd',
    plot_bgcolor='#fdfdfd',
    height=800
)

# Show the plot
fig.show()

  Cell In[16], line 1
    jupyter nbconvert --to pdf notebook.ipynb --chromium-path="C:\Users\nguye\Downloads\chrome-win\chrome-win\chrome.exe"
            ^
SyntaxError: invalid syntax

	Rank	Name	Platform	Year	Genre	Publisher	NA_Sales	EU_Sales	JP_Sales	Other_Sales	Global_Sales
0	1	Wii Sports	Wii	2006.0	Sports	Nintendo	41.49	29.02	3.77	8.46	82.74
1	2	Super Mario Bros.	NES	1985.0	Platform	Nintendo	29.08	3.58	6.81	0.77	40.24
2	3	Mario Kart Wii	Wii	2008.0	Racing	Nintendo	15.85	12.88	3.79	3.31	35.82
3	4	Wii Sports Resort	Wii	2009.0	Sports	Nintendo	15.75	11.01	3.28	2.96	33.00
4	5	Pokemon Red/Pokemon Blue	GB	1996.0	Role-Playing	Nintendo	11.27	8.89	10.22	1.00	31.37

	Rank	Year	NA_Sales	EU_Sales	JP_Sales	Other_Sales	Global_Sales
count	16598.000000	16327.000000	16598.000000	16598.000000	16598.000000	16598.000000	16598.000000
mean	8300.605254	2006.406443	0.264667	0.146652	0.077782	0.048063	0.537441
std	4791.853933	5.828981	0.816683	0.505351	0.309291	0.188588	1.555028
min	1.000000	1980.000000	0.000000	0.000000	0.000000	0.000000	0.010000
25%	4151.250000	2003.000000	0.000000	0.000000	0.000000	0.000000	0.060000
50%	8300.500000	2007.000000	0.080000	0.020000	0.000000	0.010000	0.170000
75%	12449.750000	2010.000000	0.240000	0.110000	0.040000	0.040000	0.470000
max	16600.000000	2020.000000	41.490000	29.020000	10.220000	10.570000	82.740000

Introduction¶

1. Import Libraries and Data¶

EDA¶